folate_dietqc2
# input variables
exposure = 'folate_dietqc2'
# input data
esubset <- readRDS(glue("/media/work/gwis_test/{exposure}/data/FIGI_{hrc_version}_gxeset_{exposure}_basic_covars_glm.rds")) %>%
pull(vcfid)
input_data <- readRDS(glue("/media/work/gwis/data/FIGI_EpiData/FIGI_{hrc_version}_gxeset_analysis_data_glm.rds")) %>%
filter(vcfid %in% esubset)
if(exposure == "folate_diet400qcm") {
categorical = F
} else {
categorical = T
}
Basic covariates
covariates <- sort(c('age_ref_imp', 'sex', 'energytot_imp'))
create_forest_plot_rmarkdown(data_epi = input_data,
exposure = exposure,
covariates = covariates,
hrc_version = hrc_version,
strata = 'all',
categorical = categorical)

Basic covariates + bmi + smk_ever
# input variables
covariates <- sort(c('age_ref_imp', 'sex', 'energytot_imp', 'bmi', 'smk_ever'))
# create forest plot
create_forest_plot_rmarkdown(data_epi = input_data %>% filter(across(c(bmi,smk_ever), ~ !is.na(.)),
!study_gxe %in% c("ATBC")),
exposure = exposure,
covariates = covariates,
hrc_version = hrc_version,
strata = 'all',
categorical = categorical)

Basic covariates - Non drinkers
# input variables
covariates <- sort(c('age_ref_imp', 'sex', 'energytot_imp'))
# create forest plot
create_forest_plot_rmarkdown(data_epi = input_data %>% filter(alcohol_ref == "No"),
exposure = exposure,
covariates = covariates,
hrc_version = hrc_version,
strata = 'all',
categorical = categorical)

Basic covariates - Drinkers
# create forest plot
create_forest_plot_rmarkdown(data_epi = input_data %>% filter(alcohol_ref == "Yes"),
exposure = exposure,
covariates = covariates,
hrc_version = hrc_version,
strata = 'all',
categorical = categorical)

Basic covariates + bmi + smk_ever - Non drinkers
# input variables
covariates <- sort(c('age_ref_imp', 'sex', 'energytot_imp', 'bmi', 'smk_ever'))
# create forest plot
create_forest_plot_rmarkdown(data_epi = input_data %>% filter(alcohol_ref == "No",
across(c(bmi,smk_ever), ~ !is.na(.)),
!study_gxe %in% c("ATBC")),
exposure = exposure,
covariates = covariates,
hrc_version = hrc_version,
strata = 'all',
categorical = categorical)

Basic covariates + bmi + smk_ever - Drinkers
# create forest plot
create_forest_plot_rmarkdown(data_epi = input_data %>% filter(alcohol_ref == "Yes",
across(c(bmi,smk_ever), ~ !is.na(.)),
!study_gxe %in% c("ATBC")),
exposure = exposure,
covariates = covariates,
hrc_version = hrc_version,
strata = 'all',
categorical = categorical)

folate_diet400qcm
# input variables
exposure = 'folate_diet400qcm'
# input data
# rescale the continuous variable to model per IQR increase, median as baseline
esubset <- readRDS(glue("/media/work/gwis_test/{exposure}/data/FIGI_{hrc_version}_gxeset_{exposure}_basic_covars_glm.rds")) %>%
pull(vcfid)
input_data <- readRDS(glue("/media/work/gwis/data/FIGI_EpiData/FIGI_{hrc_version}_gxeset_analysis_data_glm.rds")) %>%
filter(vcfid %in% esubset) %>%
mutate(folate_diet400qcm = as.numeric(folate_diet400qcm))
folate_diet400qcm.iqr.median = median(input_data$folate_diet400qcm)
input_data <- input_data %>%
mutate(folate_diet400qcm = folate_diet400qcm / folate_diet400qcm.iqr.median,
folate_diet400qcm = (folate_diet400qcm - median(folate_diet400qcm)) / IQR(folate_diet400qcm))
if(exposure == "folate_diet400qcm") {
categorical = F
} else {
categorical = T
}
Basic covariates
covariates <- sort(c('age_ref_imp', 'sex', 'energytot_imp'))
create_forest_plot_rmarkdown(data_epi = input_data,
exposure = exposure,
covariates = covariates,
hrc_version = hrc_version,
strata = 'all',
categorical = categorical)

Basic covariates + bmi + smk_ever
# input variables
covariates <- sort(c('age_ref_imp', 'sex', 'energytot_imp', 'bmi', 'smk_ever'))
# create forest plot
create_forest_plot_rmarkdown(data_epi = input_data %>% filter(across(c(bmi,smk_ever), ~ !is.na(.)),
!study_gxe %in% c("ATBC")),
exposure = exposure,
covariates = covariates,
hrc_version = hrc_version,
strata = 'all',
categorical = categorical)

Basic covariates - Non drinkers
# input variables
covariates <- sort(c('age_ref_imp', 'sex', 'energytot_imp'))
# create forest plot
create_forest_plot_rmarkdown(data_epi = input_data %>% filter(alcohol_ref == "No"),
exposure = exposure,
covariates = covariates,
hrc_version = hrc_version,
strata = 'all',
categorical = categorical)

Basic covariates - Drinkers
# create forest plot
create_forest_plot_rmarkdown(data_epi = input_data %>% filter(alcohol_ref == "Yes"),
exposure = exposure,
covariates = covariates,
hrc_version = hrc_version,
strata = 'all',
categorical = categorical)

Basic covariates + bmi + smk_ever - Non drinkers
# input variables
covariates <- sort(c('age_ref_imp', 'sex', 'energytot_imp', 'bmi', 'smk_ever'))
# create forest plot
create_forest_plot_rmarkdown(data_epi = input_data %>% filter(alcohol_ref == "No",
across(c(bmi,smk_ever), ~ !is.na(.)),
!study_gxe %in% c("ATBC")),
exposure = exposure,
covariates = covariates,
hrc_version = hrc_version,
strata = 'all',
categorical = categorical)

Basic covariates + bmi + smk_ever - Drinkers
# create forest plot
create_forest_plot_rmarkdown(data_epi = input_data %>% filter(alcohol_ref == "Yes",
across(c(bmi,smk_ever), ~ !is.na(.)),
!study_gxe %in% c("ATBC")),
exposure = exposure,
covariates = covariates,
hrc_version = hrc_version,
strata = 'all',
categorical = categorical)

folate_totqc2
# input variables
exposure = 'folate_totqc2'
# input data
esubset <- readRDS(glue("/media/work/gwis_test/{exposure}/data/FIGI_{hrc_version}_gxeset_{exposure}_basic_covars_glm.rds")) %>%
pull(vcfid)
input_data <- readRDS(glue("/media/work/gwis/data/FIGI_EpiData/FIGI_{hrc_version}_gxeset_analysis_data_glm.rds")) %>%
filter(vcfid %in% esubset)
if(exposure == "folate_diet400qcm") {
categorical = F
} else {
categorical = T
}
Basic covariates
covariates <- sort(c('age_ref_imp', 'sex', 'energytot_imp'))
create_forest_plot_rmarkdown(data_epi = input_data,
exposure = exposure,
covariates = covariates,
hrc_version = hrc_version,
strata = 'all',
categorical = categorical)

Basic covariates + bmi + smk_ever
# input variables
covariates <- sort(c('age_ref_imp', 'sex', 'energytot_imp', 'bmi', 'smk_ever'))
# create forest plot
create_forest_plot_rmarkdown(data_epi = input_data %>% filter(across(c(bmi,smk_ever), ~ !is.na(.)),
!study_gxe %in% c("ATBC")),
exposure = exposure,
covariates = covariates,
hrc_version = hrc_version,
strata = 'all',
categorical = categorical)

Basic covariates - Non drinkers
# input variables
covariates <- sort(c('age_ref_imp', 'sex', 'energytot_imp'))
# create forest plot
create_forest_plot_rmarkdown(data_epi = input_data %>% filter(alcohol_ref == "No"),
exposure = exposure,
covariates = covariates,
hrc_version = hrc_version,
strata = 'all',
categorical = categorical)

Basic covariates - Drinkers
# create forest plot
create_forest_plot_rmarkdown(data_epi = input_data %>% filter(alcohol_ref == "Yes"),
exposure = exposure,
covariates = covariates,
hrc_version = hrc_version,
strata = 'all',
categorical = categorical)

Basic covariates + bmi + smk_ever - Non drinkers
# input variables
covariates <- sort(c('age_ref_imp', 'sex', 'energytot_imp', 'bmi', 'smk_ever'))
# create forest plot
create_forest_plot_rmarkdown(data_epi = input_data %>% filter(alcohol_ref == "No",
across(c(bmi,smk_ever), ~ !is.na(.)),
!study_gxe %in% c("ATBC")),
exposure = exposure,
covariates = covariates,
hrc_version = hrc_version,
strata = 'all',
categorical = categorical)

Basic covariates + bmi + smk_ever - Drinkers
# create forest plot
create_forest_plot_rmarkdown(data_epi = input_data %>% filter(alcohol_ref == "Yes",
across(c(bmi,smk_ever), ~ !is.na(.)),
!study_gxe %in% c("ATBC")),
exposure = exposure,
covariates = covariates,
hrc_version = hrc_version,
strata = 'all',
categorical = categorical)

folate_sup_yn
# input variables
exposure = 'folate_sup_yn'
# input data
esubset <- readRDS(glue("/media/work/gwis_test/{exposure}/data/FIGI_{hrc_version}_gxeset_{exposure}_basic_covars_glm.rds")) %>%
pull(vcfid)
input_data <- readRDS(glue("/media/work/gwis/data/FIGI_EpiData/FIGI_{hrc_version}_gxeset_analysis_data_glm.rds")) %>%
filter(vcfid %in% esubset)
if(exposure == "folate_diet400qcm") {
categorical = F
} else {
categorical = T
}
Basic covariates
covariates <- sort(c('age_ref_imp', 'sex', 'energytot_imp'))
create_forest_plot_rmarkdown(data_epi = input_data,
exposure = exposure,
covariates = covariates,
hrc_version = hrc_version,
strata = 'all',
categorical = categorical)

Basic covariates + bmi + smk_ever
# input variables
covariates <- sort(c('age_ref_imp', 'sex', 'energytot_imp', 'bmi', 'smk_ever'))
# create forest plot
create_forest_plot_rmarkdown(data_epi = input_data %>% filter(across(c(bmi,smk_ever), ~ !is.na(.)),
!study_gxe %in% c("ATBC")),
exposure = exposure,
covariates = covariates,
hrc_version = hrc_version,
strata = 'all',
categorical = categorical)

Basic covariates - Non drinkers
# input variables
covariates <- sort(c('age_ref_imp', 'sex', 'energytot_imp'))
# create forest plot
create_forest_plot_rmarkdown(data_epi = input_data %>% filter(alcohol_ref == "No"),
exposure = exposure,
covariates = covariates,
hrc_version = hrc_version,
strata = 'all',
categorical = categorical)

Basic covariates - Drinkers
# create forest plot
create_forest_plot_rmarkdown(data_epi = input_data %>% filter(alcohol_ref == "Yes"),
exposure = exposure,
covariates = covariates,
hrc_version = hrc_version,
strata = 'all',
categorical = categorical)

Basic covariates + bmi + smk_ever - Non drinkers
# input variables
covariates <- sort(c('age_ref_imp', 'sex', 'energytot_imp', 'bmi', 'smk_ever'))
# create forest plot
create_forest_plot_rmarkdown(data_epi = input_data %>% filter(alcohol_ref == "No",
across(c(bmi,smk_ever), ~ !is.na(.)),
!study_gxe %in% c("ATBC")),
exposure = exposure,
covariates = covariates,
hrc_version = hrc_version,
strata = 'all',
categorical = categorical)

Basic covariates + bmi + smk_ever - Drinkers
# create forest plot
create_forest_plot_rmarkdown(data_epi = input_data %>% filter(alcohol_ref == "Yes",
across(c(bmi,smk_ever), ~ !is.na(.)),
!study_gxe %in% c("ATBC")),
exposure = exposure,
covariates = covariates,
hrc_version = hrc_version,
strata = 'all',
categorical = categorical)
